Unmanned aerial vehicle detection, slice assignment and beam management

ABSTRACT

A method may include transmitting multiple antenna beams to an unmanned aerial vehicle (UAV) and determining a location of the UAV. The method may also include identifying a network slice to service the UAV, assigning the identified network slice to the UAV and performing antenna beam management for the UAV while the UAV is in flight.

BACKGROUND INFORMATION

The use of Unmanned Aerial Vehicles (UAVs), also referred to as drones,is dramatically increasing. For example, UAVs are more frequently beingused for both recreational and business purposes, such as obtainingaerial images, delivering packages, etc. In some instances, the UAVs maycommunicate with a network to send/receive information to/from otherdevices and systems, such as image data from a camera included on theUAV, command data for controlling the UAV and telemetry data associatedwith the UAV's flight. As a result, providing reliable data services toUAVs while in flight is becoming increasingly important.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary environment in which systems and methodsdescribed herein may be implemented;

FIG. 2 is a block diagram of components implemented in one or more ofthe elements of the environment of FIG. 1 in accordance with anexemplary implementation;

FIG. 3 illustrates exemplary logic components implemented in a basestation of FIG. 1 in accordance with an exemplary implementation;

FIG. 4 illustrates exemplary antenna beams transmitted in theenvironment of FIG. 1 in accordance with an exemplary implementation;

FIG. 5 illustrates exemplary logic components implemented in a UAV ofFIG. 1 in accordance with an exemplary implementation;

FIG. 6 illustrates exemplary logic components implemented in the networkslice manager of FIG. 1 in accordance with an exemplary implementation;

FIG. 7 is a flow diagram illustrating processing associated withperforming slice selection and beam management in accordance with anexemplary implementation;

FIG. 8 is a flow diagram illustrating tracking a UAV and allocatingresources to support the UAV in accordance with an exemplaryimplementation; and

FIG. 9 illustrates exemplary logic components implemented in the servicemanagement and orchestration module of FIG. 1 in accordance with anexemplary implementation.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

The following detailed description refers to the accompanying drawings.The same reference numbers in different drawings may identify the sameor similar elements.

Implementations described herein provide systems and methods fordetecting UAVs and assigning network slices to the UAVs to providenetwork services. The particular network slice assigned to a UAV may bebased on, for example, the type and/or usage associated with the UAV, aquality of service (QoS) or service level agreement (SLA) metric for asubscriber associated with the UAV. Implementations described herein mayalso perform antenna beam management, while a UAV is flying, to ensurethat the UAV has network connectivity during the flight. In someimplementations, a service provider may provide antenna beam managementbased on historical data associated with UAV flights as well as usemachine learning to identify the appropriate antenna beam from theappropriate base station to provide reliable connectivity to the UAV. Inthis manner, UAVs may be provided with reliable network services whilein flight, while also optimizing use of network resources.

FIG. 1 is a diagram illustrating an exemplary environment 100 in whichsystems and methods described herein may be implemented. Referring toFIG. 1 , environment 100 includes UAVs 110-1 to 110-N (referred tocollectively as UAVs 110 and individually as UAV 110), base stations120-1 to 120-N (referred to collectively as base stations 120 andindividually as base station 120), network slice manager 130, servicemanagement and orchestration module 135, access network 140 and providernetwork 150.

UAVs 110 may each include an aircraft (e.g., a single rotor aircraft,multirotor aircraft or fixed wing aircraft) that receives controlsignals from a controller, such as a user device (not shown) to controlthe flight of UAV 110. In some implementations, UAV 110 may includecellular communication capability (e.g., fifth generation (5G)communication capability, fourth generation long term evolution (4G LTE)communication capability, etc.) to allow UAV 110 to receive and transmitdata and information (e.g., receive commands from a remotely locatedcontrol device (not shown) to control the flight, transmit aerial imagesto a remotely located device, etc.). UAV 110 may also include a shortrange wireless communication capability (e.g., WiFi) to allow UAV 110 toreceive data from and transmit data to devices and systems locatedrelatively close to UAV 110.

Base stations 120 may be associated with a communication network, suchas a 5G network, a 4G LTE network, etc. Each base station 120 mayservice a set of user equipment (UE) devices that include UAVs 110. Forexample, base stations 120 may be part of a radio access network (RAN)that connects UAVs 110 to a provider network 150, to allow UAVs 110 toreceive telephone, data and multi-media data services.

In one implementation, base station 120 may include a 5G base station(e.g., a next generation NodeB (gNB)) that includes one or more radiofrequency (RF) transceivers. For example, base station 120 may includethree RF transceivers and each RF transceiver may service a 120 degreesector of a 360 degree field of view. Each RF transceiver may include orbe coupled to an antenna array. The antenna array may include an arrayof controllable antenna elements configured to send and receive 5G newradio (NR) wireless signals via one or more antenna beams, as describedin detail below. In some implementations, base station 120 may alsoinclude a 4G base station (e.g., an evolved NodeB (eNodeB)) thatcommunicates wirelessly with UE devices, such as UAVs 110 located withinthe service range of base station 120.

Network slice manager 130 may include computer devices, functions and/orlogic devices to assign and manage the provisioning of network slices inenvironment 100. For example, using network slicing, network slicemanager 130 may section or “slice” a physical network into multiple,virtual, end-to-end networks. Each network slice may be associated witha different type of services having different characteristics andrequirements (e.g., latency, jitter, bandwidth, etc.). The term “networkslice” or “slice” as used herein refers to a logical network including aRAN (e.g., a portion of access network 140) and a core network (e.g., aportion of provider network 150) that provides telecommunicationservices and network capabilities that can vary from slice to slice.Network slice manager 130 may provision network slices to UAVs 110 toprovide network connectivity and data services for UAVs 110, asdescribed in detail below.

Service management and orchestration (SMO) module 135 may includecomputer devices and/or logic to manage and assign resources, such asRAN resources to support UAVs 110. In an exemplary implementation, SMOmodule 135 may use historical data associated with flights of UAVs 110and determine appropriate base stations 120 and beams from the basestations 120 to support network connectivity during the flight of UAVs110. In some implementations, SMO module 135 may use artificialintelligence/machine learning to identify the appropriate base stations120 and beams to support UAVs 110 during flights, as described in detailbelow.

Access network 140 may include a RAN that includes base stations 120.Base stations 120, as described above, may service UEs, such as UAVs 110in environment 100. Access network 140 may provide a connection betweenUAVs 110 and provider network 150.

Provider network 150 may include one or more wired, wireless and/oroptical networks that are capable of receiving and transmitting data,voice and/or video signals. For example, network 160 may include one ormore public switched telephone networks (PSTNs) or other type ofswitched network. Provider network 150 may further include one or moresatellite networks, one or more packet switched networks, such as anInternet protocol (IP) based network, a software defined network (SDN),a local area network (LAN), a WiFi network, a Bluetooth network, a widearea network (WAN), a 5G network, a 4G LTE Advanced network, anintranet, or another type of network that is capable of transmittingdata. Provider network 150 may include a core network that providespacket-switched services and wireless Internet protocol (IP)connectivity to various components in environment 100, such as UAVs 110,to provide, for example, data, voice, and/or multimedia services.

The exemplary configuration illustrated in FIG. 1 is provided forsimplicity. It should be understood that a typical environment mayinclude more or fewer devices than illustrated in FIG. 1 . For example,environment 100 may include a large number (e.g., hundreds or more) ofUAVs 110, user devices that control UAVs 110 (not shown), as well asmultiple network slice managers 130 and SMO modules 135. Environment 100may also include elements, such as gateways, routers, monitoringdevices, network elements/functions, etc. (not shown), that aid inproviding data services and routing data in environment 100.

Various functions are described below as being performed by particularcomponents in environment 100. In other implementations, variousfunctions described as being performed by one device may be performed byanother device or multiple other devices, and/or various functionsdescribed as being performed by multiple devices may be combined andperformed by a single device. For example, in some implementations,network slice manager 130 may include multiple elements/functions, suchas an access and mobility management function (AMF), a network sliceselection function (NSSF) and other functions of a 5G network orfunctions of a 4G LTE network used to provision network slices, asdescribed in detail below.

FIG. 2 illustrates an exemplary configuration of a device 200. One ormore devices 200 may correspond to or be included in UAV 110, basestation 120, network slice manager 130, SMO module 135 and/or otherdevices included in environment 100. Referring to FIG. 2 , device 200may include bus 210, processor 220, memory 230, input device 240, outputdevice 250 and communication interface 260. Bus 210 may include a paththat permits communication among the elements of device 200.

Processor 220 may include one or more processors, microprocessors, orprocessing logic that may interpret and execute instructions. Memory 230may include a random access memory (RAM) or another type of dynamicstorage device that may store information and instructions for executionby processor 220. Memory 230 may also include a read only memory (ROM)device or another type of static storage device that may store staticinformation and instructions for use by processor 220. Memory 230 mayfurther include a solid state drive (SSD). Memory 230 may also include amagnetic and/or optical recording medium (e.g., a hard disk) and itscorresponding drive.

Input device 240 may include a mechanism that permits a user to inputinformation, such as a keypad, a keyboard, a mouse, a pen, a microphone,a touch screen, voice recognition and/or biometric mechanisms, etc.Output device 250 may include a mechanism that outputs information tothe user, including a display (e.g., a liquid crystal display (LCD)), aspeaker, etc. In some implementations, device 200 may include a touchscreen display may act as both an input device 240 and an output device250.

Communication interface 260 may include one or more transceivers thatdevice 200 uses to communicate with other devices via wired, wireless oroptical mechanisms. For example, communication interface 260 may includeone or more radio frequency (RF) transmitters, receivers and/ortransceivers and one or more antennas for transmitting and receiving RFdata. Communication interface 260 may also include a modem or anEthernet interface to a LAN or other mechanisms for communicating withelements in a network.

The exemplary configuration illustrated in FIG. 2 is provided forsimplicity. It should be understood that device 200 may include more orfewer components than illustrated in FIG. 2 . For example, for device200 implemented in UAV 110, device 200 may include a positioningsystem/satellite navigation system, such as a global positioning system(GPS) component, which may provide position information in relation to astandard reference frame, sensors and control circuitry to controland/or monitor the flight of UAV 110.

In an exemplary implementation, device 200 performs operations inresponse to processor 220 executing sequences of instructions containedin a computer-readable medium, such as memory 230. A computer-readablemedium may be defined as a physical or logical memory device. Thesoftware instructions may be read into memory 230 from anothercomputer-readable medium (e.g., a hard disk drive (HDD), solid statedrive (SSD), etc.), or from another device via communication interface260. Alternatively, hard-wired circuitry may be used in place of or incombination with software instructions to implement processes consistentwith the implementations described herein. Thus, implementationsdescribed herein are not limited to any specific combination of hardwarecircuitry and software.

FIG. 3 is a functional block diagram of components implemented in basestation 120 in accordance with an exemplary implementation. Referring toFIG. 3 , base station 120 may include beamforming logic 310, UAVlocation detection logic 320, UAV movement tracking logic 330, beamselection logic 340 and communication logic 350. These elements may beimplemented by processor 220 executing instructions stored in memory 230of base station 120. In alternative implementations, these components ora portion of these components may be located externally with respect tobase station 120.

Beamforming logic 310 may include one or more logic devices to generateantenna beams using an antenna array located on base station 120. Theterm “antenna beam” or “beam” as used herein refers to a radiationpattern focused in a particular direction. For example, FIG. 4illustrates exemplary antenna beams generated by beamforming logic 310according to an implementation described herein. Referring to FIG. 4 ,base station 120 may include one or more antenna arrays 410. Forexample, base station 120 may include three antenna arrays 410 that eachcover a 120 degree sector with respect to base station 120. Each antennaarray 410 may include an array of controllable antenna elementsconfigured to send and receive, for example, 5G new radio (NR) wirelesssignals. The antenna elements may be digitally controllable toelectronically tilt or steer an antenna beam in a vertical directionand/or horizontal direction. In some implementations, the antennaelements may additionally be controllable via mechanical tilting orsteering using one or more actuators associated with each antennaelement.

Each base station sector associated with each antenna array 410 mayservice k UE devices, such as UAVs 110. For example, in an exemplaryimplementation, beamforming logic 310 may simultaneously generate kantenna beams for each antenna array 410, with five beams associatedwith one antenna array 410 labeled B1-B5 shown in FIG. 4 for simplicity.It should be understood that beamforming logic 310 may generateadditional antenna beams for each antenna array 410. Each antenna beamB1-B5 may service one or more UAVs 110. For example, beam B2 may serviceUAV 110 while UAV 110 is located at position B illustrated in FIG. 4 .Position B may be associated with UAV 110 in flight, while position Afor UAV 110 may be associated with UAV 110 located on the ground.Antenna beam B2 may represent the beam having the highest signal quality(e.g., received power) as determined by UAV 110. UAV 110 may alsogenerate one or more antenna beam directed toward base station 130, suchas beam B6 illustrated in FIG. 4 .

Referring back to FIG. 3 , UAV location detection logic 320 may includelogic to detect the location (e.g., latitude, longitude, elevation abovesea level) of UAVs 110. For example, based on the particular beamselected by UAV 110 and communicated to base station 120, UAV locationdetection logic 320 may determine the location of UAV 110. As anexample, if beam B2 is selected by UAV 110 as the beam with the highestsignal strength, UAV location detection logic 320 uses the known angleat which beam B2 is transmitted and the received signal strengthmeasured by UAV 110 to determine the latitude, longitude and theelevation above sea level for UAV 110. In some implementations, thelocation, as well as the speed and direction in which UAV 110 is flyingmay be determined using timing advance (TA) information transmitted tobase station 120. As described above, in some implementations, a globalpositioning system (GPS) may be included on UAV 110. In suchimplementations, UAV 110 may transmit its location and elevation to basestation 120.

UAV movement tracking logic 330 may include logic to track the movementof UAV 110. For example, UAV movement tracking logic 330 may determinethe speed at which UAV 110 is moving, the direction of movement for UAV110, and/or the distance of the particular UAV device 110 from basestation 120 based on, for example, information obtained from UAV 110,such as the current signal strength associated with one or more of beamsB1-B5 and/or the current antenna beam being used by the particular UAV110. For example, UAV movement tracking logic 330 may determine the areacovered by each generated antenna beam based on the angle/direction andwidth of each generated antenna beam. UAV movement tracking logic 330may also receive from UAV 110 the beam ID and signal strength associatedwith the beam selected by UAV 110. UAV movement tracking logic 330 maythen determine the speed, direction, and/or distance of the particularUAV 110 from base station 120 based on the beam ID and signal strengthsof particular antenna beams measured by the particular UAVs 110. Forexample, using the received signal strength value from UAV 110 may allowUAV movement tracking logic 330 to identify the location of UAV 110 atvarious points in time. From the location at various points in time, UAVmovement tracking logic 330 may determine the speed and direction atwhich UAV 110 is flying. In other implementations, UAV movement trackinglogic 330 may determine the speed and direction at which UAV 110 isflying using GPS information transmitted to base station 120 by UAV 110.

Beam selection logic 340 may include logic to select different antennabeam patterns based on the changing speed and direction of movement,and/or distance from base station 120, of UAV 110. Beamforming logic 310may then adjust the antenna beams, generated by antenna array 410 basedon the selected antenna beam patterns. For example, beam selection logic340 may determine that UAV 110 is moving at a relatively high speed(e.g., a speed above a threshold value). In this case, beam selectionlogic 340 may then determine that UAV 110 should be serviced using awider beam than the beam currently servicing UAV 110 and signalbeamforming logic 310 to provide a wider antenna beam to service UAV110. Similarly, if UAV 110 begins moving at a slower speed or becomesrelatively stationary, beam selection logic 340 may signal beamforminglogic 310 to generate a narrower beam to service UAV 110. Using widerbeams during a flight may help avoid intra-beam handoffs while UAV 110is in flight. In addition, favorable line of sight (LOS) conditionsduring a flight enable use of wider antenna beams in an efficient mannerwith respect to use of base station 120 resources.

Communication logic 350 may include logic for communicating with devicesin environment 100. For example, communication logic 350 may transmitdata to and receive data from UAV 110, network slice manager 130 and/orSMO module 135. Communication logic 350 may also communicate with otherbase stations 120, routers and other devices in access network 140 andprovider network 150.

Although FIG. 3 shows exemplary components of base station 120, in otherimplementations, base station 120 may include fewer components,different components, differently arranged components, or additionalcomponents than depicted in FIG. 3 . In addition, in someimplementation, various functions described as being performed by basestation 120 may be performed by other devices located externally withrespect to base station 120, such as by a self-organizing network (SON)system/function and/or applications server that may communicate withbase station 120, as well as communicate with other devices in accessnetwork 140 and provider network 150.

FIG. 5 is a functional block diagram of components implemented in UAV110 in accordance with an exemplary implementation. Referring to FIG. 5, UAV 110 may include beam detection logic 510, signal strengthdetection logic 520 and communication logic 530. These elements may beimplemented by processor 220 executing instructions stored in memory 230of UAV 110. In alternative implementations, these components or aportion of these components may be located externally with respect toUAV 110.

Beam detection logic 510 may include logic to detect antenna beamstransmitted by base station 120. For example, referring to FIG. 4 , beamdetection logic 510 may detect one or more of antenna beams B1-B5. In anexemplary implementation, antenna beams B1-B5 may include beamidentifiers (IDs) associated with each of the transmitted beams. Beamdetection logic 510 may detect one or more of antenna beams B1-B5 andidentify the particular detected antenna beam using the beam identifier.

Signal strength detection logic 520 may include logic to detect thesignal strength associated with antenna beams transmitted by basestation 120 and received by UAV 110. For example, signal strengthdetection logic 520 may measure a variation in the channel quality. Thechannel quality may be measured, for example, using a channel qualityindicator (CQI) value, a signal-to-noise ratio (SNR) value, asignal-to-interference-plus-noise ratio (SINR) value, a block error rate(BLER) value, a Received Signal Strength Indication (RSSI) value, aReference Signal Received Quality (RSRQ) value, a Reference SignalReceived Power (RSRP) value, and/or using another measure of signalstrength or quality. The received signal strength may be transmitted tobase station 120 along with the corresponding beam ID, as described inmore detail below

Communication logic 530 may include one or more transceivers that UAV110 uses to communicate with other devices via wired, wireless oroptical mechanisms. For example, communication logic 530 may include oneor more RF transmitters, receivers and/or transceivers and one or moreantennas for transmitting and receiving RF data. Communication logic 530may also include a modem or an Ethernet interface to a LAN or othermechanisms for communicating with elements in a network.

Although FIG. 5 shows exemplary components of UAV 110, in otherimplementations, UAV 110 may include fewer components, differentcomponents, differently arranged components, or additional componentsthan depicted in FIG. 5 .

FIG. 6 is a functional block diagram of components implemented innetwork slice manager 130 in accordance with an exemplaryimplementation. Referring to FIG. 6 , network slice manager 130 mayinclude UAV classification logic 610, slice selection logic 620,subscriber database 630 and communication logic 640. These elements maybe implemented by processor 220 executing instructions stored in memory230 of network slice manager 130. In alternative implementations, thesecomponents or a portion of these components may be located externallywith respect to network slice manager 130.

UAV classification logic 610 may include logic to identify types of UAVs110 and classify the UAVs 110. For example, UAV classification logic 610may identify UAVs 110 based on, for example, the type and/or usageassociated with the UAV 110, a quality of service (QoS) metric orservice level agreement (SLA) metric associated with a subscriberassociated with the UAV 110, etc. For example, UAV classification logic610 may identify UAVs 110 associated with commercial uses, such as thedelivery of packages, taking aerial photographs for a business purpose(e.g., for a realtor), emergency uses (e.g., for a hospital), etc. UAVclassification logic 610 may also identify UAVs 110 associated withrecreational flying, etc. The classification regarding business orrecreational purposes may be made based on a UAV identifier thatidentifies the type/purpose of UAVs 110. UAV classification logic 610may also access subscriber database 630 to identify a QoS, SLA or otherrequirement or metric associated with a subscriber flying one or moreUAVs 110. UAV classification logic 610 may then classify the UAVs 110 indifferent categories based on their types, uses, and/or QoS or SLAmetrics, etc.

Slice selection logic 620 may include logic to select network slices toservice UAVs 110. For example, slice selection logic 620 may select aslice for a UAV 110 based on information generated by UAV classificationlogic 610. For example, slice selection logic 620 may identify a slicethat has a very low latency to service a particular UAV 110 that isperforming an important task, such as delivering equipment or being usedfor another commercial purpose, and identify a slice having higherlatency, jitter, etc., for a UAV 110 that is used or recreationalpurposes. Slice selection logic 620 may also access subscriber database630 to identify a QoS level or SLA associated with a party who hassubscribed to particular levels of service (e.g., bandwidth requirement,latency, jitter) associated with one or more UAVs 110 owned/operated bythe subscriber.

Subscriber database 630, as described above, may identifyentities/parties, and their corresponding UAVs 110, that have subscribedto particular levels of service associated with providingtelecommunications services for their UAVs 110. For example, an entityflying a commercial fleet of UAVs 110 may subscribe to telecommunicationservices provided by a service provider associated with provider network150 and access network 140. For example, the entity may contract withthe service provider to provide certain QoS and/or SLA requirementsassociated with connectivity and data services provided to UAVs 110.

Communication logic 640 may include logic for communicating with devicesin environment 100 via wired, wireless or optical mechanisms. Forexample, communication logic 640 may transmit data to and receive datafrom UAV 110, base stations 120 and SMO module 135. Communication logic640 may also communicate with other devices in access network 140 andprovider network 150. For example, communication logic 640 may includeone or more transceivers and one or more antennas for transmitting andreceiving RF data, a modem or an Ethernet interface to a LAN or othermechanisms for communicating with elements in a network.

Although FIG. 6 shows exemplary components of network slice manager 130,in other implementations, network slice manager 130 may include fewercomponents, different components, differently arranged components, oradditional components than depicted in FIG. 6 . For example, in someimplementations, slice manager 130 may include or communicate withvarious functions of a 5G core network, such as an AMF function, an NSSFfunction, etc., to perform functions described herein.

FIG. 7 is a flow diagram illustrating processing associated withenvironment 100 in accordance with an exemplary implementation.Processing may begin with one of base stations 120 establishing aconnection with UAV 110. For example, beamforming logic 310 of basestation 120-1 may transmit multiple antenna beams, such as beams B1-B5from antenna array 410, as illustrated in FIG. 4 (block 710). Each beammay include a beam ID. UAV 110 may identify the best beam with which toreceive communications from base station 120 (e.g., the beam having thehighest signal strength).

For example, assume that beam detection logic 510 of UAV 110 receivesthe beam corresponding to beam B2 and signal strength detection logic520 determines that the signal strength of beam B2 is the highest ofbeams B1-B5. In this case, beam selection logic 510 may select beam B2as the beam with which to receive communications from base station 120.UAV 110 may transmit a signal to base station 120-1 identifying beam B2,based on the beam ID, and also provide the signal strength associatedwith the received beam B2. Base station 120-1 may receive the beam IDand corresponding signal strength (block 720). Base station 120-1 maythen use the beam ID and signal strength to determine the location ofUAV 110 (block 730).

For example, based on the known angle at which beam B2 is transmittedfrom antenna array 410 and the signal strength of beam B2 received byUAV 110, and communicated to base station 120-1, base station 120-1 maydetermine the geographical location and altitude of UAV 110 (block 730).For example, UAV location detection logic 320 may determine the latitudeand longitude coordinates of UAV 110, as well as the elevation above sealevel. As described above, in other implementations, UAV locationdetection logic 320 may receive GPS information from UAV 110 identifyingthe latitude, longitude and elevation above sea level.

In each case, UAV location detection logic 320 may determine if UAV 110is located on the ground (block 740). For example, based on the altitudeabove sea level and the known altitude of base station 120, UAV locationdetection logic 320 may determine that UAV 110 is located on the groundor in the air. If UAV location detection logic 320 determines that UAV110 is located on the ground (e.g., position A in FIG. 4 ) (block740—yes), base station 120 may signal network slice manager 130 that UAV110 is on the ground/not flying. In this case, network slice manager 130may assign a default slice configuration, such as an enhanced mobilebroadband (eMBB) slice to UAV 110. Such a slice may provide adequatenetwork services to UAV 110 while UAV 110 is not flying and likely tonot be receiving and/or transmitting large amounts of data.

If, however, UAV location detection logic 320 determines that UAV 110 isnot on the ground, such as at location B illustrated in FIG. 4 (block740—no), based on the elevation and/or selected beam servicing UAV 110,network slice manager 130 may identify a particular slice to service UAV110 (block 760). For example, based on the particular type of UAV 110identified by UAV classification logic 610 (e.g., commercial UAV 110,recreational UAV 110), the particular usage associated with UAV 110(e.g., delivering packages, recreational use, etc.) and/or QoSparameters, SLA parameters etc., associated with a subscriber associatedwith UAV 110, slice selection logic 620 may select an appropriate sliceto support UAV 110 while UAV 110 is in flight. Slice manager 130 maythen assign UAV 110 to the selected slice (block 760).

Assume that UAV 110 is flying. UAV 110 may transmit telemetryinformation to base station 120 in real time while in flight. Basestation 120 may receive the telemetry information and forward thetelemetry information to network slice manager 130 (block 770). Basestation 120 and/or slice manager may store the telemetry data (block770). The telemetry information may be used to train a model associatedwith tracking UAVs 110, as described in more detail below.

Base station 120 may also apply beam management based on UAV 110'smobility and route (block 780). For example, base station 120 maydetermine that UAV 110 is flying at a relatively high speed and that awider antenna beam is needed to support UAV 110, as opposed to using anarrower beam when UAV 110 is flying slower. In each case, base station120 may provide the appropriate antenna beams to support UAVs 110 whilein flight, as described in more detail below. Base station 120 may alsoset automatic neighbor relations (ANR) with other base stations 120based on the mobility and route of UAV 110. For example, base station120 may determine signal strengths of neighboring base stations 120 in adirection corresponding to the UAV 110's flight path.

FIG. 8 illustrates beam management in environment 100 in accordance withan exemplary implementation. Processing may begin with determining thespeed of UAV 110 (block 810). For example, UAV movement tracking logic330 may determine the current speed of UAV 110. For example, UAVmovement tracking logic 330 may determine the speed, direction, and/ordistance of the UAV 110 from base station 120 based on the beam ID andsignal strengths of particular antenna beams measured by the particularUAV 110. UAV movement tracking logic 330 may also determine if the speedis greater than a threshold speed (block 820). If the speed of UAV 110is not greater than the threshold (block 820—no), beam selection logic340 may assign a narrow beam to UAV 110 or maintain the current beamassigned to UAV 110 (block 830).

If, however, the speed of UAV 110 is greater than the threshold (block820—yes), beam selection logic 340 may assign a wider beam to UAV 110(block 840). For example, beam selection logic 340 may signalbeamforming logic 310 to generate a wider beam and transmit the widerbeam to UAV 110 based on the determined speed, as well as location andaltitude of UAV 110. UAV 110 may detect the wider beam and signal basestation 120-1 that the wider beam has adequate signal strength toservice UAV 110.

Based on the particular network slice assigned to UAV 110, base station120 may also prioritize command data to be transmitted to UAV 110 overother types of data (block 850). For example, base station 120-1 maytransmit command data received from a user device remotely controllingUAV 110 in real time or near real time to UAV 110 (block 850). Thecommand data may be used to control UAV 110, such as a flight path forUAV 110. Base station 120 may also prioritize and transmit a request fortelemetry data from UAV 110 (block 850).

UAV 110 may transmit the telemetry data associated with UAV 110's flightto base station 120. Base station 120 may forward the telemetry data tonetwork slice manager 130, SMO module 135 and/or other devices inprovider network 150. For example, SMO module 135 may use the historicalflight data to train a model associated with flights of UAVs 110 (block860). For example, for a commercial UAV 110 delivering packages from awarehouse to a particular region, SMO module 135 may use artificialintelligence/machine learning to train a model to track flights of UAVs110 over a period of time and identify likely destinations. Suchtraining may aid network slice manager 130 in assigning appropriateslices for UAVs 110. Such training may also allow SMO module 135 todynamically assign and allocate RAN resources to UAV 110 based on theflight, such as perform beam management for UAV 110 (block 870).

FIG. 9 illustrates SMO module 135 in accordance with an exemplaryimplementation. In some implementations, SMO module 135 may be part of aself-organizing network (SON) system. Referring to FIG. 9 , SMO module135 may include RAN resource allocation logic 910, machine learningflight model 920, near-real time RAN intelligent controller (MC) 930 andnon-real time RAN intelligent controller 940. These elements may beimplemented by processor 220 executing instructions stored in memory 230of SMO module 135. In alternative implementations, these components or aportion of these components may be located externally with respect toSMO module 135.

RAN resource allocation logic 910 may allocate RAN resources, such aparticular base stations 120, particular antenna beams at particularbase stations 120, etc., based on information obtained from machinelearning flight model 920. For example, machine learning flight model920 may take historical telemetry data and data usage informationassociated with flights of UAVs 110 and use artificialintelligence/machine learning to predict flight path/routes associatedwith particular UAVs 110. For example, a particular UAV 110 may fly froma distribution warehouse to a destination at regular intervals. Machinelearning flight model 920 may use that historical data to predict aflight path for that particular UAV 110 at a later time. Machinelearning flight model 920 may also use historical data usage informationassociated with data transmitted to/from UAV 110 to identify likelypoints in flights when higher data usage is needed. For example, machinelearning flight model 920 may predict that UAV 110 will be transmittingimage data for a developer/builder when UAV 110 is located at aparticular location (e.g., over a new building site). In this case, RANresource allocation logic 910 may use the predicted flight path and datausage to allocate RAN resources. RAN resource allocation logic 910 mayalso signal slice manager 130 to dynamically assign a new slice to UAV110 when UAV 110 is expected to transmit large amounts of data. Forexample, UAV 110 may be assigned a lower bandwidth beam and/or networkslice to maintain connectivity during a flight, and be dynamicallyre-assigned a higher bandwidth beam and/or network slice when data usageis expected to increase.

Near-real time RIC 930 may include logic to dynamically apply RANresources to service UAVs 110 in flight. For example, RAN resourceallocation logic 910 may signal near-real time RIC 930 and dynamicallyallocate RAN resources to a UAV 110 on a particular flight. For example,while a UAV 110 is in flight, near-real time RIC 930 may determine thata wider beam is needed to support UAV 110 to avoid UAV 110 frequentlyswitching between beams. Similarly, RAN resource allocation logic 910may signal non-real time RIC 940 and dynamically allocate RAN resourcesto UAV 110 that may be on the ground or operating in a recreationalmode. For example, non-real time RIC 940 may determine that a narrowerbeam is needed for a stationary UAV 110 or UAV 110 that is movingslowly.

In some implementations, power/battery resources for UAVs 110 is afactor when flying UAVs 110 a relatively long distance or for a longperiod of time. In such situations, a UAV 110 be serviced using, forexample, using both 5G and 4G LTE protocols. For example, UAV 110 may beserviced from a dual connectivity base station 120 that includes both 4GLTE and 5G communication capabilities. In such a case, flight telemetrydata, which generally has a low bandwidth requirement may be transmittedto base station 120 at a low power level (e.g., using a low powermodulation and coding (MCS) scheme associated with, for example, a 4GLTE protocol), while higher bandwidth transmissions, such as videoimages, may be transmitted to base station 120 using a 5G protocol(e.g., using millimeter wave frequencies).

Implementations described allow a service provider to detect UAVs andassign network slices to UAVs based on the particular UAV. This allowsUAV operators to be assured of having adequate network connectivity andtelecommunications services while the UAV is flying. Implementationsdescribed herein also allow a service provider to select appropriatebase stations and antenna beams to support UAVs in flight. This allowsservice providers to optimize use of network resources while alsoensuring telecommunications support for UAVs.

The foregoing description of exemplary implementations providesillustration and description, but is not intended to be exhaustive or tolimit the embodiments to the precise form disclosed. Modifications andvariations are possible in light of the above teachings or may beacquired from practice of the embodiments.

For example, features have been described above with respect to networkslice manager 130 and SMO module 135 being separate devices/module. Inother implementations, network slick manager 130 and SMO module 135 maybe part of the same system/platform.

In addition, in some implementations, base station 120, network slicemanager 130 and/or SMO module 135 may determine if a particular UAV 110has adequate radio frequency resources (e.g., particular antennas)before assigning network slices and/or providing various antenna beamsto service the particular UAV 110 to avoid providing a network sliceand/or antenna beam that would not be fully utilized by the UAV 110.This may enable the service provider to further optimize use of networkresources.

Further, while series of acts have been described with respect to FIGS.7 and 8 , the order of the acts may be different in otherimplementations. Moreover, non-dependent acts may be implemented inparallel.

It will be apparent that various features described above may beimplemented in many different forms of software, firmware, and hardwarein the implementations illustrated in the figures. The actual softwarecode or specialized control hardware used to implement the variousfeatures is not limiting. Thus, the operation and behavior of thefeatures were described without reference to the specific softwarecode—it being understood that one of ordinary skill in the art would beable to design software and control hardware to implement the variousfeatures based on the description herein.

Further, certain portions of the invention may be implemented as “logic”that performs one or more functions. This logic may include hardware,such as one or more processors, microprocessor, application specificintegrated circuits, field programmable gate arrays or other processinglogic, software, or a combination of hardware and software.

In the preceding specification, various preferred embodiments have beendescribed with reference to the accompanying drawings. It will, however,be evident that various modifications and changes may be made thereto,and additional embodiments may be implemented, without departing fromthe broader scope of the invention as set forth in the claims thatfollow. The specification and drawings are accordingly to be regarded inan illustrative rather than restrictive sense.

No element, act, or instruction used in the description of the presentapplication should be construed as critical or essential to theinvention unless explicitly described as such. Also, as used herein, thearticle “a” is intended to include one or more items. Further, thephrase “based on” is intended to mean “based, at least in part, on”unless explicitly stated otherwise.

What is claimed is:
 1. A system, comprising: at least one devicecomprising a processor, wherein the at least one device is configuredto: transmit multiple antenna beams to an unmanned aerial vehicle (UAV),receive, from the UAV, information identifying one of the multipleantenna beams and signal strength information measured by the UAV forthe one antenna beam, wherein the signal strength of the one antennabeam has a highest signal strength of the multiple antenna beamsmeasured by the UAV, determine a location of the UAV based on thereceived information identifying the one of the multiple antenna beamsand the received signal strength information, determine a speed of theUAV based on at least one of the signal strength information measured bythe UAV for the one antenna beam and received from the UAV, or timingadvance information received from the UAV, identify a network slice toservice the UAV based on the location of the UAV, assign the identifiednetwork slice to the UAV, and perform antenna beam management for theUAV while the UAV is in flight, wherein when performing antenna beammanagement, the at least one device is configured to: identify anantenna beam to service the UAV based on the determined speed.
 2. Thesystem of claim 1, wherein when performing antenna beam management, theat least one device is further configured to: dynamically assign anantenna beam to the UAV while the UAV is in flight based on at least oneof the speed or location of the UAV, and wherein when identifying anetwork slice, the at least one device is further configured to identifythe network slice based on at least one of a quality of service level orservice level agreement for a user associated with the UAV.
 3. Thesystem of claim 1, wherein when determining the speed of the UAV, the atleast one device is further configured to: determine the speed of theUAV based on the timing advance information received from the UAV. 4.The system of claim 1, wherein when performing antenna beam management,the at least one device is further configured to: assign a first antennabeam having a first width to service the UAV when the speed of the UAVis above a threshold, and assign a second antenna beam having a secondwidth when the speed of the UAV is below the threshold, wherein thefirst width is greater than the second width.
 5. The system of claim 1,wherein the at least one device is further configured to: receivetelemetry information associated with flights of a plurality of UAVs,and train, using machine learning, a model associated with trackingflights of the plurality of UAVs using the received telemetryinformation.
 6. The system of claim 1, wherein when identifying thenetwork slice, the at least one device is further configured to:determine information corresponding to at least one of a type of UAV, ause associated with the UAV, a quality of service (QOS) level or servicelevel agreement (SLA) for a subscriber associated with the UAV, or ahistoric level of data usage associated with the UAV, and identify thenetwork slice based on the determined information.
 7. The system ofclaim 1, wherein the at least one device is further configured to:prioritize certain types of data to be transmitted to and from the UAV,and provide network resources to support the prioritization.
 8. Thesystem of claim 1, wherein the at least one device comprises a basestation and a network slice manager.
 9. The system of claim 8, whereinthe network slice manager is configured to: identify the network slicebased on historical data associated with a plurality of UAVs.
 10. Thesystem of claim 1, wherein the at least one device is further configuredto: dynamically change the network slice assigned to the UAV while theUAV is in flight based on data usage requirements associated with theUAV.
 11. The system of claim 1, wherein the at least one device isfurther configured to: determine, prior to assigning the identifiednetwork slice, whether the UAV is located on the ground.
 12. The systemof claim 1, wherein the at least one device is further configured to:determine, prior to assigning the identified network slice, radioresources associated with the UAV, and when assigning the identifiednetwork slice, the at least one device is further configured to: assignthe identified network slice based on the determined radio resources.13. A method, comprising: transmitting multiple antenna beams to anunmanned aerial vehicle (UAV); receiving, from the UAV, informationidentifying one of the multiple antenna beams and signal strengthinformation measured by the UAV for the one antenna beam, wherein thesignal strength of the one antenna beam has a highest signal strength ofthe multiple antenna beams measured by the UAV; determining a locationof the UAV based on the received information identifying the one of themultiple antenna beams and the received signal strength information;determining a speed of the UAV based on at least one of the signalstrength information measured by the UAV for the one antenna beam andreceived from the UAV, or timing advance information received from theUAV; identifying, based on the location of the UAV and at least one of aquality of service level or service level agreement for a userassociated with the UAV, a network slice to service the UAV; assigningthe identified network slice to the UAV; and performing antenna beammanagement for the UAV while the UAV is in flight, wherein performingantenna beam management comprises: identifying an antenna beam toservice the UAV based on the determined speed.
 14. The method of claim13, wherein performing antenna beam management comprises: dynamicallyassigning an antenna beam to the UAV while the UAV is in flight based onthe speed or location of the UAV.
 15. The method of claim 13, whereinthe determining the speed further comprises: determining the speed ofthe UAV based on the timing advance information received from the UAV.16. The method of claim 13, wherein performing antenna beam managementcomprises: assigning a first antenna beam having a first width toservice the UAV when the speed of the UAV is above a threshold, andassigning a second antenna beam having a second width when the speed ofthe UAV is below the threshold, wherein the first width is greater thanthe second width.
 17. The method of claim 13, further comprising:prioritizing certain types of data to be transmitted to and from theUAV; and providing network resources to support the prioritization. 18.The method of claim 13, further comprising: dynamically changing thenetwork slice assigned to the UAV while the UAV is in flight based ondata usage requirements associated with the UAV.
 19. A non-transitorycomputer-readable medium having stored thereon sequences of instructionswhich, when executed by at least one processor, cause the at least oneprocessor to: receive, from an unmanned aerial vehicle (UAV),information identifying one of multiple antenna beams and signalstrength information measured by the UAV for the one antenna beam,wherein the signal strength of the one antenna beam has a highest signalstrength of the multiple antenna beams measured by the UAV; determine alocation of the UAV based on the received information identifying theone of the multiple antenna beams and the received signal strengthinformation; determine a speed of the UAV based on at least one ofsignal strength information measured by the UAV and received from theUAV, or timing advance information received from the UAV; identify anetwork slice to service an unmanned aerial vehicle (UAV), wherein whenidentifying the network slice, the instructions cause the at least oneprocessor to: identify the location of the UAV, determine least one of aquality of service level (QoS) or service level agreement (SLA) for auser associated with the UAV, and identify the network slice based onthe identified location and the determined at least one of the QoS orSLA; assign the identified network slice to the UAV; and perform antennabeam management for the UAV while the UAV is in flight, wherein whenperforming antenna beam management, the instructions further cause theat least one processor to: identify an antenna beam to service the UAVbased on the determined speed.
 20. The non-transitory computer-readablemedium of claim 19, wherein when determining the speed of the UAV, theinstructions further cause the at least one processor to: determine aspeed of the UAV based on the timing advance information received fromthe UAV.